Location: Food Quality Laboratory
2017 Annual Report
Objectives
1: Enable new or refine commercial optical, viscometry and physical technologies that integrate indicators of starch soundness in wheat and barley.
A. Identify sources of variation in viscometry-based methodologies (e.g., falling number) that are used to indicate starch soundness, then develop and test alternative procedures to reduce precision error.
B. Develop a near-infrared spectroscopy model, or alternatively a Raman spectroscopy model, for ascertaining mixture levels of conventional and waxy hexaploid wheat.
2: Enable new, real-time rapid optical methods to measure defects and vitreousness in commercial wheat kernels.
A. Develop hyperspectral imaging procedures for identification of wheat kernels damaged by scab (Fusarium head blight), black point, heat, and frost, as defined by official inspection criteria.
B. Develop imaging system for assessing the percentage of vitreous kernels in durum and hard red spring wheat.
Approach
Determine the underlying precision of the falling number procedure under ideal conditions, that is, the test when run with samples for which sampling error has been minimized. Samples of soft white wheat and three samples of club wheat that are representative pre-harvest sprouting will be evaluated. Within each subclass, the three samples are designed to be representative of low (< 300 s), moderate (300-375 s), and high (> 375 s) falling number. All thermal conditions of the falling number instrument within a test day will be held constant as practicable, including the starting temperature of the meal-water mixture, the temperature of the instrument’s bath, the volume of water within the bath, and the mass and volume of the meal-water test sample. In addition to the runs that are based on the conventional amount of meal and water (treatment A: 7 g + 25 mL), three alternate preparations will be examined. Treatment (A, B, C, D) and wheat class or subclass (SWW, Club) will be specified as fixed factors. Samples within wheat class, grind, and portions will be specified as random factors. Minimum-width 95% confidence intervals for the variance estimates will be calculated as wil best linear unbiased predictors (BLUPs) obtained from the mixed model provide estimates of FN averages for each treatment and wheat class sample. Once the precision of the FN method under standard and modified conditions has been established, the research will proceed in two tacks. First, the issue of sampling error will be addressed. Sampling will occur at typical transfer points, such as the truck dump bin at the country elevator, railcar fill sites, and the entry point to the mill. Second, if under standard operation the procedure produces coefficients of variation greater than 3%, modifications to the mixing and heating stage will be explored in an attempt to reduce air bubble entrainment, with rheological measurements (conducted in parallel) performed using a rheometer under constant shear and heating rate conditions.
NIR calibrations for mixture levels of conventional and waxy wheat will be developed with accuracies < 5%. Both Linear and non-linear quantitative modeling will be performed on both whole kernel and ground meal binary mixtures.
In hyperspectral imaging research of wheat kernels for damage or vitreousness, PCA will be examined to identify the wavelengths at local minima and maxima which inherently possess the greatest contrast. The 10 most sensitive wavelengths will be examined in paired combinations exhaustively (45 trials) using reflectance ratios. Linear discriminant analysis will be used to identify the wavelength pair whose image band ratio produces the greatest percentage of correctly classified kernels, and so on for the next pair. Three regions of interest (ROI) on the kernel, namely the endosperm, germ, and entire kernel, will be separately examined. With each ROI, image processing will be done at the pixel level, whereby subregions of damage in the ROI are first identified; then, depending on the size of the subregion, a decision will be made on whether to categorize the ROI and/or the kernel as normal or damaged.
Progress Report
Significant progress was made in all objectives of this project, which falls under National Program 306, Component 1, Problem Statement 1.A: Define, Measure, and Preserve/Enhance/Reduce Attributes that Impact Quality and Marketability. In addressing the project’s sub-objective 1.A, we completed the analysis of how variable an important wheat quality starch-related property known as falling number is within trucks brought from the field to the first point of sale. This is important because processing and marketing decisions, especially in the export market, depend on starch integrity as measured by falling number. Wheat lots failing to meet minimum falling number contract specifications become discounted across the market chain from farmer to exporter, thus putting in jeopardy the value and reputation of U.S. wheat. Weather conditions in recent years, particularly in the U.S. Pacific Northwest, have exacerbated this starch integrity problem. We were able to show that by following a prescribed sampling procedure, falling number is highly accurate (probe-to-probe agreement within a truck of less than 4 percent) for trucks containing wheat produced under normal growing conditions. Further, even with wheat adversely affected by weather, such as by large fluctuations in temperature during grain development or by rain right before harvest, the sampling procedure is reasonably accurate (probe to probe agreement within a truck of less than 7 percent). We followed up this study by starting one that investigated an essential operating condition of the falling number procedure, this being the prevailing barometric pressure at the time of measurement. Because the procedure is essentially based on measuring the viscosity of a wheat meal and water gel contained in a glass tube immersed in boiling water bath, we know that elevation of the laboratory, and hence barometric pressure, influences the rate of heating of the meal-water mixture, which in turn influences the kinetics of the enzyme that digests the starch. What we don’t know is the extent to which the falling number is affected. This newest study, which is 50% complete as of summer 2017, is providing the numerical corrections to falling number that will allow analytical laboratories at elevations up to 5,000 feet (where water boils at 95 °C) to report values at a common (sea level) condition. These corrections will be incorporated in an official directive of the USDA on the falling number procedure as well as in the ‘approved’ methods of international cereals organizations. The last piece of work on the first objective’s first component has involved a revisiting of a question at the behest of the cereals industry of whether near-infrared spectroscopy (an entrenched technology of the cereals industry) can be used as a substitute for falling number. With more powerful instrumentation and more advanced statistical regression analyses now available compared to when this topic was investigated more than 20 years ago, we undertook a comprehensive study involving hundreds of Pacific Northwest soft white wheat accessions. Our findings indicate that the model accuracies from a spectral approach are still insufficient to replace the more direct, albeit more time consuming, viscometry-based approach of falling number for characterizing starch integrity.
For sub-objective 1.B, we completed a study on the use of near-infrared spectral imaging of intact wheat kernels to measure mixture levels of conventional wheat and very low amylose, or waxy, wheat. Although longstanding in other cereals such as corn, low amylose wheat is a much newer product, with commercial releases in the U.S. occurring the past five years. Unique processing characteristics and end-use products for waxy wheat give it a premium value in the marketplace. Because waxy wheat kernels typically have the same size, shape, and color, a rapid method for ensuring the purity of identity preserved waxy lots has been a desire of the industry. Therefore, we conducted an extensive study on the use of combined features of near-infrared reflectance spectroscopy and digital imaging, with the first method being well accepted by the cereals industry for proximate analysis (e.g., protein, moisture), and the second (imaging) method offering nondestructive evaluation at the single kernel level and the potential for sorting operations. Working with prepared mixtures of conventional and waxy wheat ranging from 0 to 100 percent, we developed three different image processing and statistical regression procedures that were equally good in predicting mixture levels to accuracies (defined as the standard deviation of the differences between known and predicted mixture levels) of between 5 and 10 percentage units. These error levels, while about half again higher than what we had previously found in datasets involving near-infrared reflectance of ground meal alone (i.e., without imaging), are still considered to be sufficiently accurate for quality control screening in the wheat trade and milling industries, and they offer the potential for the inclusion of sorting.
In the second year of this project, there is one active sub-objective of the second objective, this being on the development of hyperspectral imaging for evaluation of mold damage in wheat kernels. We developed a linear discriminant analysis model based on mean of reflectance values of the interior pixels of each kernel at three wavelengths (1100, 1197, and 1394 nm) to differentiate between sound and scab damaged kernels. Work on this continues.
Accomplishments
1. Near-IR spectral imaging for measuring mixtures of conventional and waxy wheat. Wheat consists of approximately 75% starch by weight and, when milled into flour, the percentage of starch is even greater. Composed of the large linear molecule amylose and the even larger branched molecule amylopectin, starch in conventional wheat imparts cooking and end-use properties that reflect both forms. Waxy wheat, on the other hand, with almost no amylose has unique processing characteristics that make it appealing for use in niche food and industrial products. Accompanying the release of commercial waxy wheat varieties in the U.S. in recent years has been the need in the grain trade and milling industry for a rapid method to distinguish conventional and waxy wheat kernels that to the eye look very similar. ARS researchers at Beltsville, Maryland, have developed a non-destructive approach, based on the combination of digital imaging and near-IR spectroscopy, that can identify the waxy or conventional condition at the single kernel level. Compared to Beltsville’s previous successes with near-IR procedures for determining waxy/conventional mixture, the advantage of this newest procedure lies in the fact that it is nondestructive and has the potential for use in sorting operations. These findings are of direct benefit to the grains industry.
2. A better understanding of the falling number procedure. Scientists at the USDA facility in Beltsville, Maryland, have systematically identified and quantified the sources of error in the worldwide wheat quality procedure known as falling number. This procedure is used to indirectly gauge the activity of the enzyme alpha amylase which is responsible for the breaking down of starch into simpler molecules in food processing operations as well as in digestion. Based on a measurement of viscosity of a hydrated gel of ground meal or milled flour, the procedure is sensitive to variation caused by operator, temperature, grind, and barometric pressure. The collection of a representative sample from the consignment, such as a truck load at first point of sale, also plays a role in the reliability of a falling number value. All of these factors have been studied and quantified at Beltsville. With Beltsville’s recommendations on sampling and procedural operations, the precision of the falling number method, when compared to the myriad of other cereals methods, can be maintained at a very high level. This research addresses the concerns of the U.S. wheat industry, particularly in the export market, that must use falling number in meeting sales contract specifications.
Review Publications
Delwiche, S.R., Miskelly, D. 2016. Analysis of grain quality at receival. In: Wrigley, C., Batey, I., Miskelly, D. Cereal Grains: Assessing and Managing Quality. 2nd edition. Duxford, UK: Woodhead. p.513-570.
Delwiche, S.R., Vinyard, B.T. 2017. Falling number sampling variation within trucks at first point of sale. Cereal Chemistry. 94(3):480-484.
Qin, J., Kim, M.S., Chao, K., Schmidt, W.F., Cho, B., Delwiche, S.R. 2017. Line-scan Raman imaging and spectroscopy platform for surface and subsurface evaluation of food safety and quality. Journal of Food Engineering. 198:17-27.
Mo, C., Kim, M.S., Kim, G., Lim, J., Delwiche, S.R., Chao, K., Lee, H., Cho, B. 2017. Spatial assessment of soluble solid contents on apple slices using hyperspectral imaging. Biosystems Engineering. 159:10-21.
Delwiche, S.R. 2016. Advances in the identification of adulterated cereals and cereal products. In: Downey, G., editor. Advances in Food Authenticity Testing. Duxford, UK: Woodhead. p. 491-518.
